Differential evolution with adaptive mutation strategy based on fitness landscape analysis

In recent years, many different differential evolution (DE) variants have been proposed to solve real-world optimization problems. However, the performance of them is largely determined by the selection of the mutation strategy, an approach to choose favorable mutation strategy when solving various optimization problems has attracted increasing attention recently. In this paper, we propose a DE with an adaptive mutation operator based on fitness landscape (FLDE). The application of fitness landscape to DE requires three stages. First, we analyzed the fitness landscape features of each benchmark training function, a total of 45 benchmark functions are taken from CEC2014 and 2015. Then, the relationship between three mutation strategies and fitness landscape features is trained by random forest (RF) offline. Finally, the trained RF is used to predict which mutation strategy should be utilized to perform mutation operator for each problem during the evolutionary process. Besides, a historical memory parameter adaption mechanism and population size linear reduction are applied to the FLDE. The CEC2017 benchmark set is utilized to perform the experiments, and five well-known DE variant algorithms are compared with the FLDE algorithm. The experimental results attest that the proposed FLDE algorithm is highly competitive with the other five DE algorithms. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a differential evolution algorithm with an adaptive mutation operator based on fitness landscape (FLDE), which uses machine learning to choose the optimal mutation strategy. Experimental results show that the FLDE algorithm is highly competitive with five other DE algorithms.